On the reduction of complexity in the architecture of fuzzy ARTMAP with dynamic decay adjustment

This paper presents a hybrid network (FAMDDA-T) comprising the Fuzzy ARTMAP (FAM) neural network and the Dynamic Decay Adjustment (DDA) algorithm with an online pruning strategy. Twelve benchmark datasets are used to demonstrate the effectiveness of FAMDDA-T. The results of FAMDDA-T are compared with those of FAMDDA (without pruning), and the Radial Basis Function Network with DDA (RBFN-DDA) as well as its pruning version (RBFN-DDA-T). It is observed that, when compared with other DDA-based networks, FAMDDA-T is able to form a parsimonious network structure and, at the same time, to maintain a high level of network generalization in tackling pattern classification problems.